Between local innovation and global impact: cities, networks, and the governance of climate change
Bibliographic record
Abstract
Global climate governance conducted in settings such as the United Nations Framework Convention on Climate Change (UNFCCC), Major Economies Forum, and Group of Twenty (G20) has proven incapable, to date, of generating an effective response. Greenhouse gas emissions have steadily increased since the issue was added to the global agenda in the early 1990s and prospects appear slim for a single, all-encompassing international legal agreement. Outside the formal regime, however, there are signs of dynamism as non-nation state actors engage in a variety of climate governance experiments. Cities, and city-networks such as the C40 Climate Leadership Group, represent important sources of innovation in the broader system of global climate governance: they challenge prevailing norms regarding who should govern climate change, and how coordinated governance responses can be generated. This paper presents a brief history of the C40, and assesses, drawing on ideas from network theory, some of the opportunities and limitations of networked climate governance. Recognizing that cities, and city-networks, exist within a broader multi-level governance context, the paper concludes with some thoughts related to updating Canadian federal climate policy in order to leverage and enable innovative city-network governance initiatives, address gaps in current federal climate policy, and link climate change to other, pressing issues, on the urban agenda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".